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Found 10 papers, 1 papers with code
The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA
We refer to the model-based adaptation technique proposed in this paper as CORAL+.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
Xi-Vector Embedding for Speaker Recognition
We present a Bayesian formulation for deep speaker embedding, wherein the xi-vector is the Bayesian counterpart of the x-vector, taking into account the uncertainty estimate.
Task-aware Warping Factors in Mask-based Speech Enhancement
It is easy to apply the proposed dual-warping factors approach to any mask-based SE method, and it allows a single SE system to handle multiple tasks without task-dependent training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or PLDA?
The emergence of large-margin softmax cross-entropy losses in training deep speaker embedding neural networks has triggered a gradual shift from parametric back-ends to a simpler cosine similarity measure for speaker verification.
I4U System Description for NIST SRE'20 CTS Challenge
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge.
Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification
We propose a log-likelihood ratio function for the PLDA scoring with the uncertainty propagation.
Generalized domain adaptation framework for parametric back-end in speaker recognition
The efficacy of the proposed techniques has been experimentally validated on NIST 2016, 2018, and 2019 Speaker Recognition Evaluation (SRE'16, SRE'18, and SRE'19) datasets.
Golden Gemini is All You Need: Finding the Sweet Spots for Speaker Verification
We represent the stride space on a trellis diagram, and conduct a systematic study on the impact of temporal and frequency resolutions on the performance and further identify two optimal points, namely Golden Gemini, which serves as a guiding principle for designing 2D ResNet-based speaker verification models.
Cosine Scoring with Uncertainty for Neural Speaker Embedding
Uncertainty modeling in speaker representation aims to learn the variability present in speech utterances.
